TY - JOUR
T1 - A Novel Neural-Fuzzy Method to Search the Optimal Step Size for NLMS Beamforming
AU - Orozco-Tupacyupanqui, Walter
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2003-2012 IEEE.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - This paper presents a novel algorithm based on neural networks and fuzzy logic to generate membership functions and search an approximation of the optimal step-size for Normalized Least Mean Squares (NLMS) beamforming systems. The proposed method makes a new error curve, Error Ensemble Learning (EEL), based on the final estimated value of the adaptive algorithm's mean-square-error. A fuzzy clustering method individually assigns membership values to each EEL curve coordinates. This information is fed into a neural network to generate membership functions for a fuzzy inference system. The final estimation of the optimal step-size is obtained using a group of Mamdani linguistic propositions and the centroid defuzzification method. Simulation results show that a useful approximation of the optimal step-size is obtained for different interference conditions; the evaluation results also show that a higher directivity is achieved in the radiation beam pattern.
AB - This paper presents a novel algorithm based on neural networks and fuzzy logic to generate membership functions and search an approximation of the optimal step-size for Normalized Least Mean Squares (NLMS) beamforming systems. The proposed method makes a new error curve, Error Ensemble Learning (EEL), based on the final estimated value of the adaptive algorithm's mean-square-error. A fuzzy clustering method individually assigns membership values to each EEL curve coordinates. This information is fed into a neural network to generate membership functions for a fuzzy inference system. The final estimation of the optimal step-size is obtained using a group of Mamdani linguistic propositions and the centroid defuzzification method. Simulation results show that a useful approximation of the optimal step-size is obtained for different interference conditions; the evaluation results also show that a higher directivity is achieved in the radiation beam pattern.
KW - Adaptive filters
KW - Beamforming
KW - Fuzzy logic
KW - NLMS algorithm
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=84924872004&partnerID=8YFLogxK
U2 - 10.1109/TLA.2015.7055556
DO - 10.1109/TLA.2015.7055556
M3 - Artículo
SN - 1548-0992
VL - 13
SP - 402
EP - 408
JO - IEEE Latin America Transactions
JF - IEEE Latin America Transactions
IS - 2
M1 - 7055556
ER -